Henry Rzepa's blog

Tag: Data management

  • The “Accessible” in FAIR (data).

    In a previous post, I looked at the Findability of FAIR data in common chemistry journals. Here I move on to the next letter, the A = Accessible.

    The attributes of A[cite]10.1038/sdata.2016.18[/cite] include:

    1. (meta)data are retrievable by their identifier using a standardized communication protocol.
    2. the protocol is open, free and universally implementable. 
    3. the protocol allows for an authentication and authorization procedure.
    4. metadata are accessible, even when the data are no longer available. 
    5. The metadata should include access information that enables automatic processing by a machine as well as a person.

    Items 1-2 are covered by associating a DOI (digital object identifier) with the metadata. Item 3 relates to data which is not necessarily also OPEN (FAIR and OPEN are complementary, but do not mean the same).

    Item 4 mandates that a copy of the metadata be held separately from the data itself; currently the favoured repository is DataCite (and this metadata way well be duplicated at CrossRef, thus providing a measure of redundancy). It also addresses an interesting debate on whether the container for data such as a ZIP or other compressed archive should also contain the full metadata descriptors internally, which would not directly address item 4, but could do so by also registering a copy of the metadata externally with eg DataCite.

    Item 4 also implies some measure of separation between the data and its metadata, which now raises an interesting and separate issue (introduced with this post) that the metadata can be considered a living object, with some attributes being updated post deposition of the data itself. Thus such metadata could include an identifier to the journal article relating to the data, information that only appears after the FAIR data itself is published. Or pointers to other datasets published at a later date. Such updating of metadata contained in an archive along with the data itself would be problematic, since the data itself should not be a living object.

    Item 5 is the need for Accessibility to relate both to a human acquiring FAIR data and to a machine. The latter needs direct information on exactly how to access the data. To illustrate this, I will use data deposited in support of the previous post and for which a representative example of metadata can be found at (item 4) a separate location at:
    data.datacite.org/application/vnd.datacite.datacite+xml/10.14469/hpc/5496

    This contains the components:

    1. <relatedIdentifier relatedIdentifierType="URL" relationType="HasMetadata" relatedMetadataScheme="ORE"schemeURI="http://www.openarchives.org/ore/
      ">https://data.hpc.imperial.ac.uk/resolve/?ore=5496</relatedIdentifier>
    2. <relatedIdentifier relatedIdentifierType="URL" relationType="HasPart" relatedMetadataScheme="Filename" schemeURI="filename://aW5wdXQuZ2pm">https://data.hpc.imperial.ac.uk/resolve/?doi=5496&file=1</relatedIdentifier>

    Item 6 is an machine-suitable RDF declaration of the full metadata record. Item 7 allows direct access to the datafile. This in turn allows programmed interfaces to the data to be constructed, which include e.g. components for immediate visualisation and/or analysis. It also allows access on a large-scale (mining), something a human is unlikely to try. 

    It would be fair to say that the A of FAIR is still evolving. Moreover, searches of the DataCite metadata database are not yet at the point where one can automatically identify metadata records that have these attributes. When they do become available, I will show some examples here.

    Added: This search: https://search.test.datacite.org/works?
    query=relatedIdentifiers.relatedMetadataScheme:ORE     shows how it might operate.

  • Questions about the (metadata) components of a scientific article.

    The conventional procedures for reporting analysis or new results in science is to compose an “article”, augment that perhaps with “supporting information” or “SI”, submit to a journal which undertakes peer review, with revision as necessary for acceptance and finally publication. If errors in the original are later identified, a separate corrigendum can be submitted to the same journal, although this is relatively rare. Any new information which appears post-publication is then considered for a new article, and the cycle continues. Here I consider the possibilities for variations in this sequence of events.

    The new disruptors in the processes of scientific communication are the “data“, which can now be given a separate existence (as FAIR data) from the article and its co-published “SI”. Nowadays both the “article+SI” and any separate “data” have another, mostly invisible component, the “metadata“. Few authors ever see this metadata. For the article, it is generated by the publisher (as part of the service to the authors), and sent to CrossRef, which acts as a global registration agency for this particular metadata. For the data, it is assembled when the data is submitted to a “data repository”, either by the authors providing the information manually, or by automated workflows installed in the repository or by a combination of both. It might also be assembled by the article publisher as part of a complete metadata package covering both article and data, rather than being separated from the article metadata. Then, the metadata about data is registered with the global agency DataCite (and occasionally with CrossRef for historical reasons).‡ Few depositors ever inspect this metadata after it is registered; even fewer authors are involved in decisions about that metadata, or have any inputs to the processes involved in its creation.

    Let me analyse a recent example.

    1. For the article[cite]10.1021/acsomega.8b03005[/cite] you can see the “landing page” for the associated metadata as https://search.crossref.org/?q=10.1021/acsomega.8b03005 and actually retrieve the metadata using https://api.crossref.org/v1/works/10.1021/acsomega.8b03005, albeit in a rather human-unfriendly manner.† This may be because metadata as such is considered by CrossRef as something just for machines to process and not for humans to see!
    2. Citation #22 in the above does have its own metadata record, obtainable using: 
      https://data.datacite.org/application/vnd.datacite.datacite+xml/10.14469/hpc/4751

      • This has an entry
        <relatedIdentifier relatedIdentifierType="DOI" relationType="IsReferencedBy">10.1021/acsomega.8b03005</relatedIdentifier>
        which points back to the article.[cite]10.1021/acsomega.8b03005[/cite]
    3. To summarise, the article noted above[cite]10.1021/acsomega.8b03005[/cite] has a metadata record that does not include any information about the references/citations (apart from an ambiguous count). A human reading the article can however can easily identify one citation pointing to the article data, which it turns out DOES have a metadata record which both human and machine can identify as pointing back to the article. Let us hope the publisher (the American  Chemical  Society) corrects this asymmetry in the future; it can be done as shown here![cite]10.1039/C7SC03595K[/cite]

    For both types of metadata record, it is the publisher that retains any rights to modify them. Here however we encounter an interesting difference. The publishers of the data are, in this case, also the authors of the article! A modification to this record was made post-publication by this author so as to include the journal article identifier once it had been received from the publisher,[cite]10.1021/acsomega.8b03005[/cite] as in 2 above. Subsequently, these topics were discussed at a workshop on FAIR data, during which further pertinent articles[cite]10.1002/mrc.4806[/cite], [cite]10.1006/jmre.1997.1214[/cite], [cite]10.1006/jmre.2000.2071[/cite] relating to the one discussed above[cite]10.1021/acsomega.8b03005[/cite] were shown in a slide by one of the speakers. Since this was deemed to add value to the context of the data for the original article, identifiers for these articles were also appended to the metadata record of the data.

    This now raises the following questions:

    1. Should a metadata record be considered a living object, capable of being updated to reflect new information received after its first publication?
    2. If metadata records are an intrinsic part of both a scientific article and any data associated with that article, should authors be fully aware of their contents (if only as part of due diligence  to correct errors or to query omissions)?
    3. Should the referees of such works also be made aware of the metadata records? It is of course enough of a challenge to get referees to inspect data (whether as SI or as FAIR), never mind metadata! Put another way, should metadata records be considered as part of the materials reviewed by referees, or something independent of referees and the responsibility of their publishers?
    4. More generally, how would/should the peer-review system respond to living metadata records? Should there be guidelines regarding such records? Or ethical considerations?

    I pose these questions because I am not aware of much discussion around these topics; I suggest there probably should be!

    ‡Actually CrossRef and DataCite exchange each other’s metadata. However, each uses a somewhat different schema, so some components may be lost in this transit. †JSON, which is not particularly human friendly.

  • “Richer metadata makes content more useful”

    The title of this post comes from the site www.crossref.org/members/prep/ Here you can explore how your favourite publisher of scientific articles exposes metadata for their journal.

    Firstly, a reminder that when an article is published, the publisher collects information about the article (the “metadata”) and registers this information with CrossRef in exchange for a DOI. This metadata in turn is used to power e.g. a search engine which allows “rich” or “deep” searching of the articles to be undertaken. There is also what is called an API (Application Programmer Interface) which allows services to be built offering deeper insights into what are referred to as scientific objects. One such service is “Event Data“, which attempts to create links between various research objects such as publications, citations, data and even commentaries in social media. A live feed can be seen here.

    So here are the results for the metadata provided by six publishers familiar to most chemists, with categories including;

    1. References
    2. Open References
    3. ORCID IDs
    4. Text mining URLs
    5. Abstracts

    RSC
    ACS
    Elsevier
    Springer-Nature
    Wiley
    Science

    One immediately notices the large differences between publishers. Thus most have 0% metadata for the article abstracts, but one (the RSC) has 87%! Another striking difference is those that support open references (OpenCitations). The RSC and Springer Nature are 99-100% compliant whilst the ACS is 0%. Yet another variation is the adoption of the ORCID (Open Researcher and Collaborator Identifier), where the learned society publishers (RSC, ACS) achieve > 80%, but the commercial publishers are in the lower range of 20-49%. 

    To me the most intriguing was the Text mining URLs. From the help pages, “The Crossref REST API can be used by researchers to locate the full text of content across publisher sites. Publishers register these URLs – often including multiple links for different formats such as PDF or XML – and researchers can request them programatically“. Here the RSC is at 0%, ACS is at 8% but the commercial publishers are 80+%. I tried to find out more at e.g. https://www.springernature.com/gp/researchers/text-and-data-mining but the site was down when I tried. This can be quite a controversial area. Sometimes the publisher exerts strict control over how the text mining can be carried out and how any results can be disseminated.  Aaron Swartz famously fell foul of this.

    I am intrigued as to how, as a reader with no particular pre-assembled toolkit for text mining, I can use this metadata provided by the publishers to enhance my science. After all, 80+% of articles with some of the publishers apparently have a mining URL that I could use programmatically. If anyone reading this can send some examples of the process, I would be very grateful. 

    Finally I note the absence of any metadata in the above categories relating to FAIR data. Such data also has the potential for programmatic procedures to retrieve and re-use it (some examples are available here[cite]10.1021/acsomega.8b03005[/cite]), but apparently publishers do not (yet) collect metadata relating to FAIR. Hopefully they soon will.

  • Re-inventing the anatomy of a research article.

    The traditional structure of the research article has been honed and perfected for over 350 years by its custodians, the publishers of scientific journals. Nowadays, for some journals at least, it might be viewed as much as a profit centre as the perfected mechanism for scientific communication. Here I take a look at the components of such articles to try to envisage its future, with the focus on molecules and chemistry.

    The formula which is mostly adopted by authors when they sit down to describe their chemical discoveries is more or less as follows:

    1. An introduction, setting the scene for the unfolding narrative
    2. Results. This is where much of the data from which the narrative is derived is introduced. Such data can be presented in the form of:
      • Tables
      • Figures and schemes
      • Numerical and logical data embedded in narrative text
    3. Discussion, where the models constructed from the data are illustrated and new inferences presented. Very often categories 2 and 3 are conflated into one single narrative.
    4. Conclusions, where everything is brought together to describe the essential aspects of the new science.
    5. Bibliography, where previous articles pertinent to the narrative are listed.

    In the last decade or so, the management of research data has developed as a field of its own, with three phases:

    1. Setting out a data management plan at the start of the project, often a set of aspirations together with putative actions,
    2. the day-to-day management of the data as it emerges in the form of an electronic laboratory notebook (ELN),
    3. the publication of selected data from the ELN into a repository, together with the registration of metadata describing the properties of the data.

    In the latter category, item 8 can be said to be a game-changer, a true disruptive influence on the entire process. The key aspect is that it constitutes independent publication of data to sit alongside the object constructed from 1-5. More disruption emerges from the open citations project, whereby category 5 above can be released by publishers to adopt its own separate existence. So now we see that of the five essential anatomic components of a research article, two are already starting to achieve their own independence. Clearly the re-invention of the anatomy of the research article is well under way already.

    Next I take a look at what sorts of object might be found in category 8, drawing very much on our own experience of implementing 7 and 8 over the last twelve years or so. I start by observing that in 2 above, figures are perhaps the object most in need of disruptive re-invention. In the 1980s, authors were much taken by the introduction of colour as a means of conveying information within a figure more clearly; although the significant costs then had to be borne directly by these authors (and with a few journals this persists to this day). By the early 1990s, the introduction of the Web[cite]10.1039/C39940001907[/cite] offered new opportunities not only of colour but of an extra dimension (or at least the illusion of one) by means of introducing interactivity for three-dimensional models. Some examples resulting from combining figures from category 2 with 8 above are listed in the table below.

    Examples of re-invented data objects from category 2
    Example Object title Object DOI Article DOI
    1 Figure 9. Catalytic cycle involving one amine …etc. 10.14469/hpc/1854 10.1039/C7SC03595K
    2 FAIR Data Figure. Mechanistic insights into boron-catalysed direct amidation reactions 10.14469/hpc/4919 10.1039/C7SC03595K
    3 FAIR Data table. Computed relative reaction free energies (kcal/mol-1) of Obtusallene derived oxonium and chloronium cations 10.14469/hpc/1248 10.1021/acs.joc.6b02008
    4 (raw) NMR data for Epimeric Face-Selective Oxidations … 10.14469/hpc/1267 10.1021/acs.joc.6b02008
    5 Bibliography 10.14469/hpc/1116 10.1021/acs.joc.6b02008

    Example 1 illustrates how a figure from category 2 above can be augmented with active hyperlinks specifying the DOI of the data in category 8 from which the figure is derived, thus creating a direct and contextual connection between the research article and the research data it is based upon. These links are embedded only in the Acrobat (PDF) version of the article as part of the production process undertaken by the journal publisher. Download Figure 9 from the link here and try it for yourself or try the entire article from the journal, where more figures are so enhanced.

    Example 2 takes this one stage further. The hyperlinks in the published figure in example 1 were embedded in software capable of resolving them, namely a PDF viewer. But that is all that this software allows. By relocating the hyperlink into a Web browser instead, one can add further functionality in the form of Javascripts perhaps better described as workflows (supported by browsers but not supported by Acrobat). There are three such workflows in example 2.

    • The first uses an image map to associate a region of the figure data object defined by a DOI.
    • The second interrogates the metadata specifically associated with the DOI (the same DOIs that are seen in the figure itself) to see if there is any so-called ORE metadata available (ORE= Object Re-use and Exchange). If there is, it uses this information to retrieve the data itself and pass it through to
    • the third workflow represented by a set of JavaScripts known as JSmol. These interpret the data received and construct an interactive visual 3D molecular model representing the retrieved data.

    All this additional workflowed activity is implemented in a data repository. It is not impossible that it could also be implemented at the journal publisher end of things, but it is an action that would have to be supported by multiple publishers. Arguably this sort of enhancement is far better suited and more easily implemented by a specialised data publisher, i.e. a data repository.

    Example 3 does the same thing for a table.

    Example 4 enhances in a different manner. Conventionally NMR data is added to the supporting information file associated with a journal article, but such data is already heavily processed and interpreted. The raw instrumental data is never submitted to the journal and is pretty much always possibly only available by direct request from the original researchers (at least if the request is made whilst the original researchers are still contactable!). The data repository provides a new mechanism for making such raw instrumental (and indeed computational) data an integral part of the scientific process.

    Example 5 shows how a bibliography can be linked to a secondary bibliography (citations 35 and 36 in this example in the narrative article) and perhaps in the future to Open Citations semantic searches for further cross references.

    So by deconstructing the components of the standard scientific article, re-assembling some of them in a better-suited environment and then linking the two sets of components to each other, one can start to re-invent the genre and hopefully add more tools for researchers to use to benefit their basic research processes. The scope for innovation seems considerable. The issue of course is (a) whether publishers see this as a viable business model or whether they instead wish to protect their current model of the research article and whether (b) authors wish to undertake the learning curve and additional effort to go in this direction. As I have noted before, the current model is deficient in various ways; I do not think it can continue without significant reinvention for much longer. And I have to ask that if reinvention does emerge, will science be the prime beneficiary?

  • FAIR data ⇌ Raw data.

    FAIR data is increasingly accepted as a description of what research data should aspire to; Findable, Accessible, Inter-operable and Re-usable, with Context added by rich metadata (and also that it should be Open). But there are two sides to data, one of which is the raw data emerging from say an instrument or software simulations and the other in which some kind of model is applied to produce semi- or even fully processed/interpreted data. Here I illustrate a new example of how both kinds of data can be made to co-exist.

    I will start with a recent publication[cite]10.1002/anie.201407165[/cite] with the title Crystallographic Snapshot of an Arrested Intermediate in the Biomimetic Activation of CO2. The nature of this intermediate caught the eye of another research group, who responded with their own critique[cite]10.1002/anie.201411654[/cite]‡ along with the comment “However, since we have no access to the original crystallographic data …” They might have been referring to the semi-processed data (containing the so-called hkl structure factors) but they may also have been alluding to the raw image data captured directly from the diffractometer cameras. That traditionally has not been available via the CSD (Cambridge structural database), but would be required for a complete re-analysis of the crystal structure. Now the first example of how both FAIR (processed) data and raw data can co-exist has appeared.

    The latest version of the CSD database shows an entry resulting from the following publication[cite]10.1021/acsomega.7b00482[/cite] and the deposited data has its own DOI there (10.5517/ccdc.csd.cc1n9ppb). That entry in turn has a DOI pointer to the Raw data (10.14469/hpc/2300) held in a different location and the pointer is reciprocated (⇌) with the latter pointing back to the former. Both datasets point to the original article, thus completing a holy triangle.†

    There is more. The Raw dataset (10.14469/hpc/2300) declares it is a member of a superset, called Crystal structure data for Synthesis and Reactions of Benzannulated Spiroaminals; Tetrahydrospirobiquinolines (10.14469/hpc/2297) where you can find information about six other related structures. That collection is in turn a member of a superset called Synthesis and Reactions of Benzannulated Spiroaminals; Tetrahydrospirobiquinolines (10.14469/hpc/2099) where DOIs to other types of data associated with this project can be found, such as Computational data (10.14469/hpc/2098) and NMR data (10.14469/hpc/2294). Although a human can with some determination follow these associations up, down and across, the system is designed to also be followed by automated algorithms that could traverse this web quickly and efficiently.

    So you can now see that a crystal structure held in the CSD could be the starting point for a journey of FAIR data discovery, in manner that has not hitherto been possible. How quickly the CSD will become populated by links to Raw (and other) data remains to be seen. I have not yet discovered any mechanism for specifying a CSD query which stipulates that Raw data must be available, but no doubt this will come.

    To end, back to the Biomimetic Activation of CO2 referred to at the start. With no access to the original data, recourse was made to computational modelling.[cite]10.1002/anie.201411654[/cite] Which where  I came in, since I wanted access to the original (computational) data. Sadly it did not appear to be available with the article,[cite]10.1002/anie.201411654[/cite] in much the same manner as the original complaint. Perhaps, when FAIR data becomes fully accepted as part of how science is done nowadays, such complaints will become ever rarer!

    ‡In fact the original authors did respond[cite]10.1002/anie.201504197[/cite] with an acknowledgement that their original conclusions were not correct.

    †Almost. The article [cite]10.1021/acsomega.7b00482[/cite] cites DOI: 10.14469/hpc/2099 (Ref 28), but it does not cite DOI: 10.5517/ccdc.csd.cc1n9ppb because the latter had not been minted yet at the time the final proofs were corrected, and there is no mechanism to add it at a later stage.

  • The provenance of scientific data – establishing an audit trail.

    In an era when alternative facts and fake news afflict us, the provenance of scientific data becomes ever more important. Especially if that data is available as open access and exploitable by others for both valid scientific reasons but potentially also by those with other motives. Here I consider the audit trail that might serve to establish data provenance in one typical situation in chemistry, the acquisition of NMR instrumental data. 

    Here I describe how such data is generated in my department; details may vary elsewhere.

    1. The prospective user of the NMR service is allocated a service ID. In our case, that ID relates to the research group rather than to individual researchers. This ID is parochial, it does not reference any other information about the user in the institute. Only the service manager has the information to associate this ID with real users and this information is normally not distributed.
    2. When a sample is submitted, this ID is used to create a new folder containing the data as a sub-folder of the group ID and located on the NMR data servers.
    3. The dataset itself‡ contains a number of files that contain an audit trail (names such as audita.txt, auditp.txt) with the fields: ##AUDIT TRAIL= $$ (NUMBER, WHEN, WHO, WHERE, PROCESS, VERSION, WHAT). Typically, none of these files have propagated the original user ID under which the data was collected; to do so would require a programmatic connection between the local authentication systems and the spectrometer software used, a connection that is normally missing. Thus the first break in the provenance trail.
    4. In principle other audit trails can be inferred from these files, such as the unique identity of the instrument provided by its manufacturer. Further information such as e.g. the probe used to collect the data (probes can be readily changed over) or any calibration data used in setting up the instrument for the data collection are by and large not recorded. To my knowledge, although an instrument can have a unique serial number, such serial numbers of swappable components such as probes are not recorded by the collection software. Thus the second break in the provenance trail.
    5. This data then needs to be processed by further software. In this case we use the MestreNova system for this task. Each dataset has editable assigned properties; below I show those that can be associated with the spectrum (accessed with MestreNova using Edit/Properties). All this comes from the information collected by the instrument. The user’s identity can be inserted into the “title” field, the display of which is off by default. 
    6. There is also a section for parameters, a synonym for which might be metadata and accessed using this program from View/Tables/Parameters. If Author was entered as a parameter in the dataset by the spectrometer software, the Mnova document would retrieve that information. Equally, an ORCID identifier for the author entered at the time of data collection and thus stored in the dataset could be read by Mnova, stored and displayed if configured to do so. It would be fair to say however that this option is rarely if indeed ever systematically implemented by NMR instrument data collection software and so is never propagated to the data processing software (as highlighted in red below). Thus a third break in the provenance trail.
      This is also an alternative and this time formal metadata field that can be populated, by default as shown below with the type of spectrum and nucleus. These properties are not controlled in the sense of only allowing those terms that are present in a specified dictionary. The jargon for such control is a metadata schema. This is not used here, since dissemination of this information is not intended; the software accepts whatever information it is given. 
      There are thus several opportunities to collect the identity of the experimenter and thus attribute provenance to the collected data, but this does very much depend on the will of researchers, institutions or publishers to enforce specific policies around this. The fourth break in the provenance trail.
    7. The dataset can then be uploaded (DOI: 10.14469/hpc/1291), at which stage provenance can finally be added using the ORCID credentials of the person publishing the dataset, who of course may or may not be the person who actually recorded the data! The full metadata for this specific collection can be seen at data.datacite.org/10.14469/hpc/1291. Or to put it another way, this is the first point in the provenance chain where the metadata is controlled by a schema and is also discoverable in a standard programmatic manner, i.e. the preceding link. The provenance is now formally associated with the ORCID identifier using the DataCite metadata schema. You should be aware that a local policy† is that access to the repository at https://data.hpc.imperial.ac.uk is only allowed by cross-authentication with http://orcid.org/ using the user’s ORCID. This identifier is then automatically propagated to the metadata held at e.g. data.datacite.org/10.14469/hpc/1095. Currently however, none of any metadata originally recorded in either the instrumental file set or the processed MestreNova file is forwarded on to the metadata record held at DataCite; again loss of information and potentially of provenance. 
    8. The peer-reviewed article resulting from the interpretation of this data however can be associated with the provenance introduced in the previous stage; see data.datacite.org/10.14469/hpc/1267  and the IsReferencedBy property. 

    Now imagine if there was a common thread in all the stages of acquiring, processing and publishing this scientific data based on the ORCID. 

    1. Providing an ORCID could be made an essential requirement of access to the instrument.
    2. This information would be propagated to the dataset …
    3. by inclusion in one or more of the audit trail files.
    4. At this stage, further persistent identifiers associated with the instrument manufacturer could be added, which help identify not only the instrument used, but sub-components such as the changeable probe. This would allow access to any calibration curves or probe sensitivity and other aspects.
    5. The ORCID and other relevant information could be picked up by the software used to convert the data into spectra and propagated into the metadata containers for this software …
    6. where its use is controlled by a specified schema.
    7. At this stage, the ORCID and information such as the nucleus recorded, the sample temperature etc can be propagated on to the final metadata records.
    8. And the reader of the article describing this work would have a formally defined provenance audit trail they could follow back to the start of the experiment or forward to a published article. In this case, the data claims provenance (acquired from peer review) from the article, but it should also work in reverse with the article claiming provenance from the data on which it is based. The indexing of this bidirectional exchange is one of the exciting features that we should see emerging from CrossRef (holders of metadata about articles) and DataCite (holders of metadata about research data) in the near future.

    We are clearly a little way from having the infrastructures described above for establishing such data audit trails. To do so will require cooperation from instrument manufacturers, at least in the example as charted above, as well as researchers, institutions, publishers, peer-reviewers and funding bodies. The first step would be to ensure that all scientists who intend collecting, processing and publishing data should claim an ORCID. That remark is directed specifically at undergraduate, postgraduate and post-doctoral researchers, not just at their supervisor or their PI (principal investigator). At a point when the discussion about alternate facts and perhaps even alternate data risks a general loss of confidence in science, we should be pro-active in establishing trust in the scientific processes.

    ‡ You can see an example obtained by this process at DOI: 10.14469/hpc/1095

    † This requirement is a strong driver for the uptake of ORCID amongst our student population.

  • Data-free research data management? Not an oxymoron.

    I occasionally post about "RDM" (research data management), an activity that has recently become a formalised essential part of the research processes. I say recently formalised, since researchers have of course kept research notebooks recording their activities and their data since the dawn of science, but not always in an open and transparent manner. The desirability of doing so was revealed by the 2009 "Climategate" events. In the UK, Climategate was apparently the catalyst which persuaded the funding councils (such as the EPSRC, the Royal Society, etc) to formulate policies which required all their funded researchers to adopt the principles of RDM by May 2015 and in their future researches. An early career researcher here, anxious to conform to the funding body instructions, sent me an email a few days ago asking about one aspect of RDM which got me thinking.

    The question related to the divide between data as a separate research object (and which therefore has to be managed), and data as an inseparable part of the article narrative, which is of course ostensibly managed by the journal publication processes. Such data may often be the description of a process rather than simply tables of numbers or graphs. In chemistry it may include chemical names and chemical terms as part of an experimental procedure. For one nice illustration of such embedded data, go look at the chemical tagger page. Here the data is blending with the semantics, and the two are not easily separated. So, when such separation is not easily achieved, should the specific processes required by RDM as illustrated in the five bullet points below actually be followed?

    1. Specify a data management plan to be followed, as for example points 2-5 below.
    2. Decide upon a location for your data, separated into one for "live" or working data (the purpose simply being to ensure it is properly backed up) and the other for a sub-set of formally "published data" which has to be available for at least ten years after its publication.
    3. Use 2 to gather metadata (see 6-14 below) and in return get a DOI representing the location of the combined metadata + data, from a suitable registration authority such as DataCite.
    4. Quote this DOI(s) in the article describing the results of analysing the data and presenting hypotheses, and conversely once the article itself is allocated its own DOI from a registration authority such as CrossRef, update the metadata in item 3 so as to achieve a bidirectional link between the data and its narrative (and we assume that DataCite and CrossRef will also increasingly exchange the metadata they each hold about the items).
    5. Add both the data and the article DOIs to any institutional CRIS or current research information system (parenthetically, I regard this last stipulation as rather redundant if items 3 and 4 are working effectively, but its a good interim measure whilst the overall system matures).

    So, should step 2 be included if the data itself is inextricably intertwined with the narrative and cannot be separated? The slightly surprising advice I would suggest is yes! And the answer is that it IS possible to generate metadata (data about the, possibly entwined, data) which CAN be processed in such a step. What forms would such metadata take?

    1. Identification of the researcher(s) involved. This would nowadays take the form of an ORCID (Open Research and Collaborator Identifier).
    2. Identification of the hosting institution where the data has been produced. There is currently no equivalent to an ORCID for institutions, but it is very likely to come in the future.
    3. A date stamp formalising when the (meta)data is actually deposited.
    4. A title for the project being described. Here we see a blurring between the narrative/article and the data; a title is the shortest possible description of the narrative/article, and it may also apply to the data object(s) or it could have its own title.
    5. A slightly fuller abstract of the project being described. Here we see further blurring between the narrative and data objects.
    6. One can include "related identifiers", in particular the DOIs of any other relevant articles that might have been published which may expand the context of the data, and also the DOIs of any other relevant datasets which may have been allocated in step 2 above.
    7. It is also beneficial to include "chemical identifiers". These can take the form of InChI strings and InChI keys, which allow discretely defined molecular objects which were the object of the research to be tracked and which relate to both the narrative and any other data objects.
    8. If specific software has been used to analyse data, it too can be included as a "related identifier" (e.g. [cite]10.5281/zenodo.19272[/cite]
    9. Potentially at least, if a well-defined instrument has been involved, it too could be included with its own "related identifier". With both 13-14, other issues may need addressing, such as versioning etc, but this no doubt will be sorted in due course.
    10. etc.

    So items such as 6-14 can be collected and sent to e.g. DataCite with a DOI received in return as part of item 2 of the RDM processes. No "pure" data need be involved, only metadata. Nonetheless such metadata can only increase the visibility and discoverability of the research, as illustrated in how such metadata can be searched for the components described above.

  • Metametadata: data about data about (chemical) data.

    Scientists are familiar with the term data, at least in a scientific or chemical context, but appreciating metadata (meaning "after", or "beyond") is slightly more subtle, in the sense of using it to mean data about data. The challenge lies in clarifying where the boundary between data and its metadata lies and in specifying and controlling the vocabulary used for these metadata descriptions. Items in a chemical metadata dictionary might include e.g. subject classifications such as Organic Molecular Chemistry or identifiers such as InChIkey. But what could metametadata be? Here I briefly show some examples by way of illustration.

    Let me start by defining a data repository as a store of both data and the metadata describing it. The metadata is to be exposed in a standard manner which allows it to be aggregated by other agencies. Nowdays, it is becoming common to identify such a data object together with its metadata using a persistent identifier, or DOI. But to decide if any particular repository and the data objects contained therein is generally useful to you, you need information about the metadata itself. Technically, this is defined using a schema[cite]10.2312/re3.008[/cite] describing the metadata (which might e.g. identify any dictionaries used); hence metametadata. Now you need to store the metametadata and so I introduce the concept of a registry which does this. This metametadata object is itself assigned a DOI‡ and here I list these DOIs for a personal selection of some chemically oriented examples, in this case deriving from the largest registry of research data repositories re3data.org. You can search for your own entry at their site: http://service.re3data.org/search.

    Data repository The repository metametadata DOI♣ Badge
    Figshare 10.17616/R3PK5R[cite]10.17616/R3PK5R[/cite] NH3-8
    Zenodo 10.17616/R3QP53[cite]10.17616/R3QP53[/cite] NH3-8
    Cambridge structure database 10.17616/R36011[cite]10.17616/R36011[/cite] NH3-8
    Crystallographic open database 10.17616/R37S31[cite]10.17616/R37S31[/cite] NH3-8

    Oxford University Research Archive

    		10.17616/R3Q056[cite]10.17616/R3Q056[/cite] NH3-8

    Open Notebook Science

    		10.17616/R3859D[cite]10.17616/R3859D[/cite] NH3-8

    Usefulchem

    		10.17616/R3Z89N[cite]10.17616/R3Z89N[/cite] NH3-8

    Chemotion

    		10.17616/R34P5T[cite]10.17616/R34P5T[/cite] NH3-8

    Chemspider

    		10.17616/R38P4P[cite]10.17616/R38P4P[/cite] NH3-8

    Chemical Database Service

    		10.17616/R36P42[cite]10.17616/R36P42[/cite] NH3-8

    Imperial College HPC data repository.

    		10.17616/R3K64N[cite]10.17616/R3K64N[/cite],[cite]10.14469/hpc/382[/cite] NH3-8

    Imperial College SPECTRa repository.[cite]10.1021/ci7004737[/cite]

    		10.17616/R30316[cite]10.17616/R30316[/cite] NH3-8

    Not all of the repositories listed in the table above assign formal DOIs to their data collections, meaning that the metadata for their entries cannot be aggregated in a searchable manner using e.g. search.datacite.org/ui (or search.datacite.org/api for the machine version). Currently, the metametadata does not fully carry this information, an aspect which I gather will be rectified in a future revision of the re3data schema.[cite]10.2312/re3.008[/cite]

    Importantly, both metadata and (repository) metametadata can be searched using APIs (application programmer interface), ensuring that the entire flow of meta information can be subject to automated software analysis rather than just visual inspections by a human.This should allow a rich and open infrastructure for handling research objects or data to be built up using hierarchical metadata. The examples above indeed show that the chemical space is already the largest component of the Natural Sciences space.

    NH3-8

    Although the edifice is still largely in its infancy, already I think we can start to see an alternative open approach emerging to "Googling" for data, or the even older traditional bespoke (i.e. non-open) services offered by commercial human-based abstractors of chemical metadata.

    ‡This DOI is information about the metametadata, and hence it is metametametadata, or m3data. Sorry! ♣The citations at the foot of this post are generated entirely automatically (by a WordPress plugin called Kcite) from the m3data associated with each entry, i.e. the DOI listed. Were the persistent identifier for the entry ever to be changed, this would propagate automatically to the citation, unlike the static entries in the table.

     

  • Personal web pages on digital repositories.

    The university sector in the UK has quality inspections of its research outputs conducted every seven years, going by the name of REF or Research Excellence Framework. The next one is due around 2020, and already preparations are under way! Here I describe how I have interpreted one of its strictures; that all UK funded research outputs (i.e. research publications in international journals) must be made available in open unrestricted form within three months of the article being accepted for publication, or they will not be eligible for consideration in 2020.

    At the outset, I should say that one infrastructure to help researchers adhere to the guidelines is being implemented in the form of the Symplectic system. This allows a researcher to upload the final accepted version of a manuscript. At Imperial College, a digital repository called Spiral serves this purpose and also acts as the front end for collecting informative metadata to enhance discoverability. The final accepted version is then converted by the publisher into a version-of-record. This contains styling unique to the publisher and the content is subjected to further scrutiny by the authors as proof corrections. In an ideal world, these latter changes should also be faithfully propagated back to the final accepted version, as would all the supporting information associated with the article. Since most authors do not exactly enjoy the delights of proof corrections, this final reconciliation of the two versions may not always be assiduously undertaken.

    I became concerned about the existence of two versions of any given scientific report and that the task of ensuring total fidelity in the content of both versions may negatively impact on the author’s time. Much better if the publisher could grant permission for the author to archive the version-of-record into a digital repository.

    Some experiments were needed, and I decided to start them in reverse, by archiving my oldest publications. Since Symplectic now provides a system to do this, I began by using it. Symplectic identifies each publisher’s policies for archival, of which the most liberal are known as ROMEO GREEN. To quote from the definition, this colour allows the author to “archive pre-print and post-print or publisher’s version/PDF“. In an afternoon I had processed most of my ROMEO green articles. You know how it is sometimes, you do not read the fine print! And so the library soon informed me that archival of ROMEO GREEN was in fact only permitted on the author’s “personal web page”. Spiral, as an institutional repository, does not apparently constitute a personal web page for me and so none of my Symplectic submissions could be accepted for archival there.

    Time to rethink the experiment. Firstly, I very much wanted the reprints to be held by a proper digital repository rather than a conventional web page. Why? I wanted my reprints to adhere as much as possible to FAIR: findable, accessible, interoperable and re-usable. Well, at least the first two of those (the last two relate more to data). A repository is designed to hold metadata in a formal and standards-based manner and metadata helps achieve FAIR. So I asked the Royal Society of Chemistry (as a ROMEO GREEN publisher) whether a personal web page hosted on a digital repository would qualify. I was soon informed  that I had proposed a neat solution here, and they couldn’t see an issue.

    Now, all I had to do is find a repository where I could create such a personal web page. The chemistry department at Imperial College has for ten years hosted a DSpace repository called SPECTRa[cite]10.1021/ci7004737[/cite] which already has the functionality for individuals to create personal collections. I had also picked up on the increasing attention being given to Zenodo, like the World-Wide Web itself an offshoot of CERN (of large Hadron Collider fame) and born from the need for researchers to more permanently archive the outputs of their researches. These outputs include software, videos, images, presentations, posters, publications and (most obviously for CERN) datasets. I thought I would include them in my experiment as well. There results are summarised below.

    DSpace-SPECTRa Zenodo
    Community Henry Rzepa personal web page
    reprint collection    		 Rzepa personal computational
    chemistry data and reprint page
    Collection Royal Society of Chemistry reprints
    Publication 10042/195577 10.5281/zenodo.18758[cite]10.5281/zenodo.18758[/cite]
    Thesis 10044/1/20860[cite]http://doi.org/10044/1/20860[/cite] 10.5281/zenodo.18777[cite]10.5281/zenodo.18777[/cite]
    Dataset 10.14469/ch/191342[cite]10.14469/ch/191342[/cite] 10.5281/zenodo.18632[cite]10.5281/zenodo.18632[/cite]
    Harvesting OAI-ORE OAI-PMH

    The last line of this table includes a link to another design feature of a repository, facilitating the ability to harvest the content. The ContentMine project (“The right to read is the right to mine!“) has shown how such harvesting of facts from the literature can be automated on a vast scale, and (IMHO) represents an example of those disruptive innovations that have the power to change the world forever. It also enshrines the idea that scientific facts funded by the public purse should be capable of being openly liberated from their containers. A harvestable repository seems an ideal container for achieving this.

    My experiment is part of what might be seen as the increasingly subtle interplay between:

    • scientific authors, whose creative endeavour research is and without whom scientific publishers would not exist
    • publishers who create a business model from the content freely given them by authors but also (especially if a commercial publisher) need to be accountable to their shareholders.
    • the funding councils, many of whom now wish the outcomes of the research they fund to be openly available to all
    • the local libraries/administrators who have to adhere to/enforce all the rules contractually handed down to them by publishers whose direct customers they are, but who also need to serve their community of readers and authors.
    • researchers who would rather do research than fret about the above, and who would rather spend limited resources doing that research rather than diverting an increasing amount of their attention into the above system.
    • readers, who need unimpeded access to the research endeavours of others, but often have little influence on the policies and actions of all the other stakeholders, since they are  NOT considered customers (of the publishers).
    • etc. etc.

    My experiment was in part designed to explore these rules, their interpretations and their boundaries. For the time being at least I seem to have found an arrangement that allows me to distribute versions-of-record of my own work, thanks to a generous and far-sighted learned society publisher. Watch this space!

    Acknowledgments

    This post has been cross-posted in PDF format at Authorea.

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